Deep image priors When solving linear inverse problems using the alternating direction method of multipliers (ADMM) algorithm, Venkatakrishnan et al. [1]} observed that it results in two decoupled optimizations. The first one enforces the data prior while the second enforces data fidelity to the observation. The first step can be thought of as a denoising problem, thus, a denoiser can be employed to solve this step thereby avoiding the need for an explicit image prior. Venkatakrishnan et al. [1]} use denoisers like BM3D [3]} in ADMM setting for image restoration. Inspired by this, recent methods propose learning-based proximal operators for the denoising step of ADMM. OneNet by Chang et al. [4]}, CNN denoiser by Zhang et al.[5]}, Meinhardt et al. [6]} . In this work, we compare our explicit natural image prior based MAP inference with the learned proximal operator of OneNet. Our evaluations show that our results are superior to OneNet. It is important to note that OneNet's proximal operator uses adversarial loss [7]} which is known to result in sharper recovery of details.
